PUST: Transferring Update Signals via Proxy Models
KnowledgeXLab · hf · 2026-07-14
This paper introduces a novel post-training paradigm, Proxy-guided Update Signal Transfer (PUST), aiming to decouple "exploration" from "distribution alignment".
Core Idea
- Instead of making the main model bear the expensive exploration costs directly, a lightweight proxy model is used to first search for high-reward behaviors.
- The relative improvement signal before and after the proxy's optimization is extracted and transferred to the main model for policy alignment.
Main Benefits
- Reduces exploration costs and supports asynchronous generation, caching, and reuse of optimization signals.
- It transfers "relative improvements" rather than absolute distributions, naturally supporting weak-to-strong improvement and cross-model transfer.
Experimental Conclusions
- Systematically evaluated on math and coding tasks using Qwen3-family models.
- Results show that update signals from significantly weaker proxies can still consistently improve stronger main models with tunable performance gains.
The authors reframe post-training from a "monolithic online optimization" process into a more modular, reusable, and cost-effective pipeline.
Related event: PUST: New Paradigm Uses Small Models to Guide Large Model Training(2 posts)→
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